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chore: clean up old code comments

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dunkeroni 2024-03-18 12:37:24 -04:00 committed by psychedelicious
parent 2af9286345
commit 8b30cbe81e
1 changed files with 0 additions and 19 deletions
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invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -87,27 +87,8 @@ class AddsMaskGuidance:
gradient_mask: bool gradient_mask: bool
def __call__(self, latents: torch.Tensor, t: torch.Tensor, conditioning) -> torch.Tensor: def __call__(self, latents: torch.Tensor, t: torch.Tensor, conditioning) -> torch.Tensor:
#output_class = step_output.__class__ # We'll create a new one with masked data.
# The problem with taking SchedulerOutput instead of the model output is that we're less certain what's in it.
# It's reasonable to assume the first thing is prev_sample, but then does it have other things
# like pred_original_sample? Should we apply the mask to them too?
# But what if there's just some other random field?
#prev_sample = step_output[0]
# Mask anything that has the same shape as prev_sample, return others as-is.
# return output_class(
# {
# k: self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v
# for k, v in step_output.items()
# }
# )
return self.apply_mask(latents,t) return self.apply_mask(latents,t)
# def _t_for_field(self, field_name: str, t):
# if field_name == "pred_original_sample":
# return self.scheduler.timesteps[-1]
# return t
def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor: def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
batch_size = latents.size(0) batch_size = latents.size(0)
mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size) mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size)